Our first release is a powerful large language model (LLM) trained on over 48 million papers, textbooks, reference material, compounds, proteins and other sources of scientific knowledge. You can use it to explore the literature, ask scientific questions, write scientific code, and much more.
I’m prepping for an ask me anything session on Deep Reinforcement Learning, so I decided to ask it to…
Explain deep reinforcement learning to a two year old
Absoluely amazing. While it definitely missed the mark, maybe there is some opportunity to tool-combine…
Reinforcement learning is a method of teaching a machine how to do something by having it try different things and rewarding it when it does something correctly. For example, if you wanted to teach a machine how to play chess, you would start by giving it a chess board and some pieces. The machine would then try different moves and be rewarded each time it made a move that led to a win. Over time, the machine would learn which moves are most likely to lead to a win, and eventually it would become quite good at playing chess.
Deep learning is a method of teaching a machine how to do something by using multiple layers of information. For example, if you wanted to teach a machine to recognize pictures of animals, you would start by showing it a lot of pictures of animals. The machine would then try to identify patterns in the pictures, and the more pictures it saw, the better it would become at identifying animals.
Ok this is pretty dope, thanks for sharing. It’s interesting to see how these two will work together. Gonna try to write some posts in this manner and see what happens
And the last statement for Deep Learning reads: Neural Networks are.
I can agree with that!
Cool stuff, @harpreet.sahota! Gonna be cool to have a personal scientific assistant in the future when doing research and working on specialized ML problems
